A layman’s guide to Attribution Models

The understanding of attribution models requires a basic knowledge of some digital marketing jargons and concepts. Let us quickly go through some of them.

Ad

A short description of a product or an idea or an event aimed to create an awareness for a particular brand, strategically.

Campaign

A collection of ads through which an agency wants to convey an idea or a message to the users. It generally has a theme around it; aimed at creating an appeal for a set of products to target users.

View or Impression Event

Whenever a user sees an ad; a view or an impression event is triggered.

Click Event

Whenever a user clicks on an ad; click event is triggered.

Conversion Event

Whenever a user makes some sort of transaction; i.e., buying a product, downloading an app, filling a form etc., conversion event is triggered.

User Journey

It is a series and timeline of views and clicks which led to a particular conversion. It can also be termed as the path to conversion.

To visualize everything, consider this example:

I want to buy a mobile phone; so I randomly googled ‘best mobile phone under 30k’; google showed an ad for a device called Moto Z play. I clicked on it and went through its specs but I decided against buying it due to some reason.

Now that google knows my history; it will keep showing me relevant ads even on some third party sites. I encountered one of those ads but didn’t click on it. After a week or so; an ad popped up on my facebook newsfeed that there is a 20% discount on Moto Z play on amazon, at this moment I decided to buy. So I clicked on this facebook ad and was about to buy this product from amazon; but before that, I checked for discount coupons on Coupon-Dunia, and finally, I bought the product for 25k.

User journey for this particular conversion will be something like this: Click1 (Google, Campaign1, Ad1), View1 (Display, Campaign2, Ad2), View2 (Facebook, Campaign3, Ad3), Click3 (Facebook, Campaign3, Ad4), View3 (Display, Campaign4, Ad5) -> Conversion (Amazon, Revenue:25000)

Now that we have a basic idea about ads, campaigns, impressions/views, clicks, conversions and user journey; we can dive deeper into attribution.

What is attribution?

In simple terms, the process of identifying which ad/ campaign led to conversion is called attribution. Attribution is necessary to understand how your ads/campaigns are performing on different search, social, and display networks; using this insight you can plan the budget allocation and targeting rules for different campaigns. It gives a clear picture in terms of ROI and lead generation.

Attribution can be performed using different models; these models can be categorized into two types: 1) Single-Touch Models and 2) Multi-Touch Models

Single-Touch Models

The philosophy of single-touch models is very simple; attribute the conversion to only one event; either the first event or the last event. These are generally ‘click only‘ models; views are not given credit for the conversions.

First-Touch (First-Click)

The entire credit for the conversion will go to the first event in this model. In our case, if we consider the above user journey; entire conversion weight and revenue will be distributed to Click1 (Google, Campaign1, Ad1).

Last-Touch (Last-Click)

The entire credit for the conversion will go to the last event in this model. In our case, if we consider the above user journey; entire conversion weight and revenue will be distributed to Click3 (Facebook, Campaign3, Ad4). If you look carefully at our user journey; the last event was View3 (Display, Campaign4, Ad5) but as this is a ‘click only‘ model we will give credit to the last click.

So this is all about single-touch models; they are very easy to understand and fairly easy to implement. It makes sense when your use case is simple and you are using singular campaigns on a fixed network. It is not reliable when multiple engines and multiple campaigns are involved; the weight and revenue distribution, in this case, will not give you correct insight on your ad/campaign performance vs spending!

Multi-Touch Models

Multi-Touch models credit each and every event (both clicks and views) that leads to conversion. Weight distribution varies from model to model, but it validates the contribution of each event for conversion!

There are four commonly used multi-touch models: 1) Linear 2) U-Shape 3) Time-decay 4) Custom. We will go through them one by one in detail.

Linear

In Linear model, weights are evenly distributed amongst all the events. It gives an equal pie of revenue to each ad/campaign which contributed to conversion.

Linear model weight distribution with graph

In our case, there are total 5 events; two clicks and three views, each of these events will get an equal weight and the corresponding ad/campaign will get fifth part of revenue!

U-Shape

In U-shape model, weights are assigned in the ratio of 40-20-40; it means that the first and last event will get 40% weight and the remaining events will get an equal distribution of 20% weight.

U-Shape model weight distribution with graph

In our case, Click1 (Google, Campaign1, Ad1) and View3 (Display, Campaign4, Ad5) will get 40% each. View1 (Display, Campaign2, Ad2), View2 (Facebook, Campaign3, Ad3) and Click3 (Facebook, Campaign3, Ad4) will get (20/3)% each!

This model is quite unique as compared to above models; it gives more credit to edge events, edge events are the ones which actually starts or stops a user journey. So from a campaign strategy perspective, these are the events which actually called for an action in the real sense.

Time-decay

In Time-decay model, the weight of the event is inversely proportional to the time difference between the event date and conversion date. In other words, the event closer the conversion will get higher weights and the event further to the conversion will get lesser weights.

Time-decay model weight distribution with graph

In our case, Click1 (Google, Campaign1, Ad1), View1 (Display, Campaign2, Ad2), View2 (Facebook, Campaign3, Ad3), Click3 (Facebook, Campaign3, Ad4), View3 (Display, Campaign4, Ad5) will get credit in increasing order; i.e., Click1<View1<View2<Click3<View3.

From a theoretical viewpoint, Time-decay seems to be the most sensible weight distribution mechanism! But in reality, the model selection depends on the type of business, type of product, and use-case of the client. For some businesses, U-Shape will make more sense while for other Time-Decay will be more preferable.

Custom

There are times when a certain business solution or strategy would require meticulous analytics information. U-Shape and Time-Decay are used widely for attribution but they may not be enough! At this point, if we want to dive one level deeper we can design a custom model, which may assign weights according to the type of events, campaigns and ads, it may also consider the timeline of the events.

Sometimes there are events which do not lead to conversion; these are called negative paths. We can use this data to design a custom algorithm which assigns and updates weights at entity (engine, campaign, ad) levels depending upon the event type (click or view).

There are some engines/ channels which are not that important in a user journey; it doesn’t matter whether this engine is a part of the path or not! In our case, last event View3 (Display, Campaign4, Ad5) was triggered because as a user I tried to find discount coupons on Coupon-Dunia; this event is relevant but doesn’t add value to the path because conversion would still happen even if this event is not present in the path! It still gets higher weights in Time-Decay and U-Shape. These discrepancies can be removed in the custom algorithmic model after we have enough data to find and connect these invisible dots!

Time-decay vs U-Shape vs Linear

To wrap things up, it is really difficult to say that a particular model is 100% perfect and efficient; you need to try and figure out which model works better for you. Ideally to derive optimum insights; a combination of two or three models should be tried and this data should be then used to design a custom algorithmic model to cater your needs!

Stay tuned for more insights!

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